Operation of automatic machines occurs, at least, when the operator or user of an automatic machine uses the machine to perform more complex tasks, in many cases based on an interaction conducted between the user and the machine. One example to be considered here is automatic ticket machines that allow the sale of tickets not only for a few specific routes but also for a larger network, for example a national rail system, in consideration of a wide variety of boundary conditions. The user is interactively instructed by the automatic machine to indicate the starting point of his/her journey, the destination, and the desired departure or arrival time, and if applicable to provide further information such as, for example, a preferred seat location, smoking/non-smoking and the like. For this purpose, the machine prompts him/her, for example, by way of a visual display, acoustically, or in audiovisual form. Depending on the configuration of the system, the user inputs the corresponding information by individual buttons, a keypad, other manual control elements, or in spoken form. Modern systems embodied as speech interaction systems are even capable of extracting, from the user's complete sentences, specific data such as departure location or destination. Many systems in which the request to the user to input appropriate information is made in spoken form have the additional capability for interrupting the speech commands (so-called speech prompts) outputted by the system, thus shortening the interaction time.
It is apparent from practical use, however, that users of such interaction systems have very different inhibition thresholds when dealing with automatic machines, and in some cases behave very differently when interacting with the machine. Users can be very roughly divided, for example, into experienced and inexperienced users. Whereas the experienced user deals with a particular interaction system in a practiced and confident manner, and his/her priority is to achieve the purpose of the interaction (i.e. having his/her requests fulfilled) as quickly as possible, for the inexperienced user a gentler interaction that never makes him/her uncertain may be more important. A much greater differentiation between users also exists, depending on the complexity of the system and the tasks to be performed by it, in terms of the personal requirements associated with users and in terms of the users' behavior. It is desirable in this context for automated interaction system to be made as flexible as possible.
Solutions have therefore already been proposed in which the execution of an interaction conducted between the user and the automatic machine or interaction system is adapted to user characteristics and/or to user behavior. Such solutions are often aimed at a direct or immediate adaptation of the interaction to a particular user who is operating the machine. U.S. Pat. No. 5,493,608, for example, describes a solution in which the response speed of speech prompts of an interaction system is adapted to the speed at which the particular user is speaking.
It has also been described, however, not to adjust the interactive behavior of an automated interaction system to the individual user, but rather to adapt it to a group of users, the individual user then being allocated to one of the groups known to the system. A corresponding approach is described, for example, in Komatani, K., et al. in “User Modeling in Spoken Dialogue Systems for Flexible Guidance Generation,” Proceedings of EUROSPEECH 2003, GENEVA. According to the aforesaid document, users are allocated to various classes on the basis of their behavior, and the speech interaction is adapted in accordance with the class allocated to a user. Both the nature of the classes (in the document, a subdivision into the classes of experienced users, inexperienced users, and rush users is made) and the adaptation performed in terms of the different classes are, however, absolutely rigid. In other words, there is one fixed interaction path for experienced users, another for inexperienced users, and lastly a further, but still static, interaction path for rush users. The corresponding allocation to user classes, and their linkage to the individual interaction paths, is predefined by the developer of the interaction or the system.
If the interaction is adapted using Bayes networks, as is done inter alia in Hjalmarsson, A., “Adaptive Spoken Dialogue Systems,” Centre of Speech Technology, KTH, January 2005, then application of the corresponding underlying statistical procedures and methods requires an enormous calculation effort that often also slows down the execution time of such interactions. In such methods, the interactions and their structures are differentiated by so-called network graphs whose edges, as described in European Patent No. 1 102 241 A1, have transition probabilities from one interaction state to another allocated to them.
Group-based or user-class-based information systems that manage access to information with reference to class have furthermore been described. One example of these is permissions management, known from computer systems and networks, in which the rights of the individual users participating in the system to access portions of the system are controlled and managed. A corresponding solution is described, for example, in German Patent No. 694 273 47 T2. Additionally known in this context are solutions for managing user classes.
Also previously described are speech recognition systems that, from the speech inputs of a user, can determine the language he/she is speaking but can also, for example, draw conclusions as to age or gender. With the aid of emotion detectors associated with speech recognition systems, statements can even be made regarding the user's emotional states such as anger, impatience, or frustration.